In [1]:
import pandas as pd
import seaborn as sns

C:\Users\Daan\Anaconda3\lib\site-packages\statsmodels\tools\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm
In [2]:
import plotly.express as px

import matplotlib.pyplot as plt
In [3]:
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib

For this excercise, we have written the following code to load the stock dataset built into plotly express.

In [4]:
stocks = px.data.stocks()
stocks.head()
Out[4]:
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708

Question 1:

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

In [5]:
stocks.plot(x = 'date', y = 'NFLX', figsize = (12, 8))
plt.ylabel('stock value')
plt.title('Stock value Netflix')
plt.legend('',frameon=False);

Question 2:

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

In [6]:
stocks.plot(x ='date', figsize = (12, 8), style = ['-', '.', '-.', 'o', '--', ':'])
plt.ylabel('stock value')
plt.title('Stock values');

Seaborn

First, load the tips dataset

In [7]:
tips = sns.load_dataset('tips')
tips.head()
Out[7]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

Question 3:

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

  • Are there differences between male and female when it comes to giving tips?
  • What attribute correlate the most with tip?
In [8]:
fig = sns.barplot(x = 'day', y = 'tip', hue = 'sex', data = tips)
fig.set(ylabel = 'avg of tip')
plt.legend(bbox_to_anchor=(1.1, 1), loc=2, borderaxespad=0.)
plt.title('What is the average tip given by men and women per day of the week?');

Plotly Express

Question 4:

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset

Hints:

  • Turn stocks dataframe into a structure that can be picked up easily with plotly express
In [9]:
stocks = px.data.stocks()
fig = px.line(stocks, x='date', y=stocks.columns[1:], title='Stock values')
fig.update_layout(yaxis_title = 'stock value', legend_title = '')
fig.show()

The tips dataset

In [10]:
tips = px.data.tips()
fig = px.histogram(tips, x = 'day', y = 'tip',
             color='sex', barmode='group',
             histfunc='avg', category_orders={'day': ['Thur', 'Fri', 'Sat', 'Sun'], 'sex' : ['Male', 'Female']})
fig.show()

Question 5:

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [11]:
#load data
df = px.data.gapminder()
df.head()
Out[11]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4
In [12]:
df = df.drop(df[df['year'] != 2007].index)
In [13]:
fig = px.histogram(df, 
                   x = 'pop', 
                   y = 'continent', 
                   color = 'continent', 
                   category_orders={"continent": ["Asia", "Africa", "Americas", "Europe", "Oceania"]}, 
                   text_auto = '.2s'
                  )
fig.update_traces(textposition = 'outside')
fig.update_layout(xaxis_title = 'pop', showlegend = False)
fig.show()
In [ ]: